114-0480/01 – Applied econometrics (APE)
| Gurantor department | Department of Economics | Credits | 5 |
| Subject guarantor | doc. Ing. Jiří Balcar, Ph.D. | Subject version guarantor | doc. Ing. Jiří Balcar, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2020/2021 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The aim of the course is to apply econometric methods to real problems. Emphasis is placed on the perspective of professional users of econometrics and illustrates how empirical researchers consider and apply econometric methods. The aim is to equip students with broad and rigorous tools to (i) conduct an independent econometric analysis of the problems they may encounter in their work and (ii) make recommendations where appropriate.
Teaching methods
Tutorials
Summary
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
ISP: Processing of a set of tasks through a seminar paper, test.
For other students the same conditions apply.
E-learning
Other requirements
Course requirements include active class participation and home-works, and a final exam.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to economics and Stata, and its use for descriptive statistics.
2. Least squares method, linear regression and OLS estimator properties.
3. Credibility of estimation, hypothesis testing, measurement errors and feedback in the presence of stochastic variables.
4. Interpretation and comparison of models (including model selection criteria).
5. Basics of forecasting and simulation.
6. Heteroskedasticity and autocorrelation.
7. Principles of time series analysis and volatility (conditional and variance modeling).
8. Endogenity, estimation using instrumental variables.
9. Logit and probit models.
10. Multinomial models and models of ordered answers.
11. Integer data (Poisson regression model, negative binomial model, general integer regression), duration.
12. Tobit models (censored variables), treatment effects.
13. Linear models of panel data: fixed and random effects.
14. Linear models of panel data: static and dynamic models, incomplete panels (/ attrition), tests of non-stationarity and cointegration.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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